class_docstr: Randomly drop some regions of feature maps.
  
   Please refer to the method proposed in `DropBlock
   <https://arxiv.org/abs/1810.12890>`_ for details.
  
  Args:
      drop_prob (float): The probability of dropping each block.
      block_size (int): The size of dropped blocks.
      warmup_iters (int): The drop probability will linearly increase
          from `0` to `drop_prob` during the first `warmup_iters` iterations.
          Default: 2000.
code: def __init__(
      self, drop_prob, block_size, warmup_iters=2000, **kwargs
  ):
      super(DropBlock, self).__init__()
      assert block_size % 2 == 1
      assert 0 < drop_prob <= 1
      assert warmup_iters >= 0
      self.drop_prob = drop_prob
      self.block_size = block_size
      self.warmup_iters = warmup_iters
      self.iter_cnt = 0
  
file_path: mmdetection/mmdet/models/layers/dropblock.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmdet.registry import MODELS
label_desc: Use image node_id 1 to create a new DropBlock object from inherited base classes: nn with example: obj = DropBlock(drop_prob, block_size, warmup_iters)